On Mon, Jan 12, 2009 at 2:37 PM, Sébastien Morin
<sebastien.morin.1@xxxxxxxxx> wrote:
Hi,
I am now planning to implement the code for calculating R2eff from peak
intensities before passing R2eff values to the curve fitting code...
I thought of a function like:
=================
def calc_r2eff(self, exp_type='cpmg', id=None, delayT=None,
int_cpmg=0, int_ref=0):
"""Calculate the effective transversal relaxation rate from the
peak intensities. The
equation depends on the experiment type chosen, either 'cpmg' or
'r1rho'.
If 'cpmg' is chosen, the equation used is:
r2eff = - ( 1 / delayT ) * log ( int_cpmg / int_ref )
If 'r1rho' is chosen, nothing happens yet, as the code is not
implemented.
"""
if exp_type == 'cpmg':
r2eff = - ( 1 / delayT ) * log ( int_cpmg / int_ref )
return r2eff
=================
This design looks good. It's implementation will be a bit tricky
though and will require much thought, as you'll find out ;)
I would put this one in 'specific_fns'. However, I am still not sure
about how this function should be called.
Yep, it should be a method of the Relax_disp object.
The function should be called after data input (also after using
exp_type() and cpmg_delayT()), and before any curve fitting. This
function should be called for every spin (in a loop), for every
experiment id (in a loop, which would include different spectrometer
frequencies as well as different cpmg_delayT, if present).
Is that a good approach ?
Is there something important I forget ?
It should be called only when required. This is at the very start of
Relax_disp.calc(), Relax_disp.back_calc(), Relax_disp.grid_search(),
and Relax_disp.minimise(). Alternatively you could create a new user
function, something like relax_disp.calc_r2eff(). It will need
careful checks to make sure all the required data is input into relax.
Regards,
Edward
Re: Relaxation dispersion -- calculate R2eff